Characteristics and dynamic analysis of the February 2021 long-runout disaster chain triggered by massive rock and ice avalanche at Chamoli, Indian Himalaya

A massive rock and ice avalanche occurred on the western slope of the Ronti Gad valley in the northern part of Chamoli,Indian Himalaya,on 7 February 7,2021.The avalanche on the high mountain slope at an elevation of 5600 m above sea level triggered a long runout disaster chain,including rock mass avalanche,debris avalanche,and flood.The disaster chain had a horizontal travel distance of larger than 17,600 m and an elevation difference of 4300 m.In this study,the disaster characteristics and dynamic process were analyzed by multitemporal satellite imagery.The results show that the massive rock and ice avalanche was caused by four large expanding discontinuity planes.The disaster chain was divided into five zones by satellite images and field observation,including source zone,transition zone,dynamic entrainment zone,flow deposition zone,and flood zone.The entrainment effect and melting water were recognized as the main causes of the long-runout distance.Based on the seismic wave records and field videos,the time progress of the disaster was analyzed and the velocity of frontal debris at different stages was calculated.The total analyzed disaster duration was 1247 s,and the frontal debris velocity colliding with the second hydropower station was approximately 23 m/s.This study also carried out the numerical simulation of the disaster by rapid mass movement simulation(RAMMS).The numerical results reproduced the dynamic process of the debris avalanche,and the mechanism of long-runout avalanche was further verified by parametric study.Furthermore,this study discussed the potential causes of disaster and flood and the roles of satellite images and seismic networks in the monitoring and early-warning.

Spatial and Temporal Differences of Climate Suitability of Ice and Snow Sports in Major Ski Tourism Destinations in China

Ice and snow tourism in China has grown significantly since the country successfully hosted the Beijing Winter Olympics.Climatic conditions profoundly impact the development of ice and snow tourism;however,most studies have focused on constructing different climate suitability indicators for ice and snow tourism to evaluate individual regions,lacking horizontal comparative studies across multiple regions.This study aims to enrich the connotation of climate suitability for ice and snow sports,establish an evaluation model based on snowfall amount,temperature,and wind speed,and use daily meteorological data from 1991 to 2021 to horizontally compare the climate suitability for ice and snow sports in major ski tourism destinations in China.This study boasts four major findings:1)the average ice and snow sports climate index of each region decreases over time,and the overall suitability of the climate for ice and snow sports is reducing;2)northern Xinjiang exhibits the most evident regional differentiation from‘very suitable’to‘generally suitable’;3)the spatial zoning of climate suitability for ice and snow sports exhibits heterogeneity,as northern Xinjiang is divided into two‘suitable and above’zones with rotating empirical orthogonal function(REOF).Correspondingly,the four provinces of Hebei,Heilongjiang,Jilin,and Liaoning are divided into three‘generally suitable and above’zones;4)snowfall amount is the main factor affecting the climate suitability of ice and snow sports in the major ski tourist destinations in China.

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged periods.Thus,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV modules.To address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected images.Furthermore,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition process.This process utilizes both the external ellipse method and the pixel statistical method to refine the identification process.The effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area pictures.The results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV modules.In conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV modules.This breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and ice.By proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

A World of Ice and Snow Being also Invaluable Asset:A Case Study of the"Snow Village"

Based on field visit and interview,the current situation of snow village in China is summarized from four aspects:core scenic spots in snow village,skiing industry in snow village,film and television industry in snow village,and ice and snow agritainment.The investigation found that there are still significant problems in homogenization,scenic area infrastructure,and government regulation in snow village.Targeted solutions are proposed from four aspects:tapping internal advantages,strengthening top-level design and infrastructure construction,promoting tourism industry upgrading,and collaborating to innovate the ice and snow tourism supply chain,in order to further promote the economic development of snow village.

Spatial Mismatch of Tourism Resources and Tourism Economy:A Case Study of Ice and Snow Tourism in Altay Region

The coordinated development of tourism resources and tourism economy is vital improve the quality and efficiency of regional tourism.Taking the ice and snow tourism in Altay region as an example,this study constructed an evaluation indicator system and used the methods of gravity center model,spatial mismatch index model and obstacle degree model to calculate the spatial mismatch index and influencing factors of ice and snow tourism resources and tourism economy in Altay region as a whole and in each county and city.It found in the study period:(i)the ice and snow tourism resources and tourism economy in Altay region was synchronous in the development of time sequence,while the spatial distribution pattern was characterized by regional imbalance;(ii)at the macro scale,the spatial mismatch degree of the ice-snow tourism resource center and the tourism economic center in Altay region was gradually reduced,but they were always separated from the spatial geometric center of Altay region;(iii)at the micro scale,the spatial mismatch index of counties and cities in Altay region was mainly negative mismatch,and the types of spatial mismatch changed in different degrees;(iv)the factors showed certain spatial differentiation characteristics in different counties of Altay region,and there were some differences in the obstacles of different mismatch level areas,and the economic basis was the most frequent obstacle in different mismatch level areas of Altay region.

Evaluation of Forest Damaged Area and Severity Caused by Ice-snow Frozen Disasters over Southern China with Remote Sensing

The accurate assessment of forest damage is important basis for the forest post-disaster recovery process and ecosystem management. This study evaluates the spatial distribution of damaged forest and its damaged severity caused by ice-snow disaster that occurred in southern China during January 10 to February 2 in 2008. The moderate-resolution imaging spectroradiometer(MODIS)13 Q1 products are used, which include two vegetation indices data of NDVI(Normalized Difference Vegetation Index) and EVI(Enhanced Vegetation Index). Furtherly, after Quality Screening(QS) and Savizky-Golay(S-G) filtering of MODIS 13 Q1 data, four evaluation indices are obtained, which are NDVI with QS(QSNDVI), EVI with QS(QSEVI), NDVI with S-G filtering(SGNDVI) and EVI with S-G filtering(SGEVI). The study provides a new way of firstly determining the threshold for each image pixel for damaged forest evaluation, by computing the pre-disaster reference value and change threshold with vegetation index from remote sensing data. Results show obvious improvement with the new way for forest damage evaluation, evaluation result of forest damage is much close to the field survey data with standard error of only 0.95 and 1/3 less than the result that evaluated from other threshold method. Comparatively, the QSNDVI shows better performance than other three indices on evaluating forest damages. The evaluated result with QSNDVI shows that the severe, moderate, mild damaged rates of Southern China forests are 47.33%, 34.15%, 18.52%, respectively. By analyzing the influence of topographic and meteorological factors on forest-vegetation damage, we found that the precipitation on freezing days has greater impact on forest-vegetation damage, which is regarded as the most important factor. This study could be a scientific and reliable reference for evaluating the forest damages from ice-snow frozen disasters.

Impact of the Shrinkage of Arctic Sea Ice on Eurasian Snow Cover Changes in 1979-2021

Recent research has shown that snow cover induces extreme wintertime cooling and has detrimental impacts.Although the dramatic loss of Arctic sea ice certainly has contributed to a more extreme climate,the mechanism connecting sea-ice loss to extensive snow cover is still up for debate.In this study,a significant relationship between sea ice concentration(SIC)in the Barents-Kara(B-K)seas in November and snow cover extent over Eurasia in winter(November-January)has been found based in observational datasets and through numerical experiments.The reduction in B-K sea ice gives rise to a negative phase of Arctic Oscillation(AO),a deepened East Asia trough,and a shallow trough over Europe.These circulation anomalies lead to colder-than-normal Eurasian mid-latitude temperatures,providing favorable conditions for snowfall.In addition,two prominent cyclonic anomalies near Europe and Lake Baikal affect moisture transport and its divergence,which results in increased precipitation due to moisture advection and wind convergence.Furthermore,anomalous E-P flux shows that amplified upward propagating waves associated with the low SIC could contribute to the weakening of the polar vortex and southward breakouts of cold air.This work may be helpful for further understanding and predicting the snowfall conditions in the middle latitudes.

Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data

The snow depth on sea ice is an extremely critical part of the cryosphere.Monitoring and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate change.The Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea ice.By comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow depth.This method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow layers.Based on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea ice.The new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the U.S.National Snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.

Risk prevention and control strategies for the severely affected areas of snow disaster in the Three Rivers Source Region(TRSR), China

Historically,frequent and heavy snow disaster(SD)has caused serious livestock death and casualties,resulting in a devastating impact on animal husbandry development in the Three Rivers Source Region(TRSR).From winter in 2018 to spring in 2019,the largest SD occurred in this area over the past 10 years,especially in core zones of the Lancang River Source Region.Field research results show that the main causes of the major SD include weak infrastructure(i.e.,roads,communications,warm sheds,and insufficient forage reserve),low rate of domestic animals for sale before the SD,and low loss settlement rate.SD occurrence could furtherly reduce the ability of disaster prevention,mitigation and relief of disaster loss.In the future,heavily affected SD areas should improve the forecasting ability of snowfall incidents,strengthen infrastructure construction,implement grass and livestock balance strategies,optimize livestock structure,improve loss settlement rate,and develop a modern compound model of animal husbandry development model that combines breeding,slaughtering and deep processing of animal product.

Assessment of the Mechanical Properties of Carbon-Fiber Heating Cables in Snow and Ice Melting Applications

The use of carbon-fiber heating cables(CFHC)to achieve effective melting of snow and ice deposited on roads is a method used worldwide.In this study,tensile and compressive tests have been conducted to analyze the mechan-ical properties of the CFHC and assess whether the maximum tensile and compressive strengths can meet the pavement design specifications.In order to study the aging produced by multiple cycles of heating and cooling,in particular,the CFHC was repeatedly heated in a cold chamber with an ambient temperature ranging between-20℃ and+40℃.Moreover,to evaluate how the strength of the pavement is affected by its presence,the CFHC was embedded at different depths and concrete blocks with different curing ages were subjected to relevant com-pression and splitting tensile tests.Numerical simulations based on the ANSYS software have also been performed and compared with the outcomes of the static loading tests.The results show that the CFHC embedded in the concrete does not affect the compressive splitting tensile strengths of the pavement.Overall,the CFHC meets the conditions required for continued use in road ice melting applications.

Vulnerability Assessment of Snow Disaster Based on Traffic System:A Case Study of Chenzhou City in Hunan Province,China

The article establishes the patterns of urban snow disaster system and disaster chain based on the theory of regional disaster system. The patterns indicate that urban snow disaster is exacerbated mainly through the traffic system. In addition, the paper sets up the vulnerability assessment index system and synthetically vulnerability assessment model of urban snow disaster which are mainly based on traffic system, and applies them in Chenzhou City. The results of assessment indicate that obvious geographical differences exist in the vulnerability of snow disaster bearing bodies: vulnerability of Chenzhou section of the Beijing-Zhuhai expressway is the highest in Chenzhou City, and the southeastern counties are more vulnerable than the northwest region. Furthermore, according to the snow disaster vulnerability dynamic process analysis, the vulnerability of Chenzhou City obviously increased in 2008 winter compared with that in 2007. Finally, the paper presents some suggestions for the locations of the emergency commands and the reserves of relief materials based on the evaluation results, and points out that disaster monitoring and relevant technical level should be strengthened for the minimization of traffic system's vulnerability.

A Review of Thermo- and Diffusio-Phoresis in the Atmospheric Aerosol Scavenging Process. Part 2: Ice Crystal and Snow Scavenging

The role of phoretic forces in the identification of particles acting as ice nuclei in mixed phase cloud is discussed. A method used to identify the effective ice nucleating particles is to sample ice crystals, which are afterwards sublimated, and to examine the particles remaining after evaporation. The procedure takes into account only crystal with a maximum diameter of 20 μm, by assuming that small crystals do not scavenge aerosol during growth, and therefore that crystals contain only the effective nucleating particles. This assumption is questionable, however, as experiments have shown that even small ice crystals can scavenge aerosol. Another approach has been to compare the number and elemental composition of residual particles in small ice crystals and of aerosol near the cloud. By considering as example soot and black carbon aerosol, contradictory conclusions on their importance in the processes of ice nucleation have been reported in the literature. We suggest that, in addition to physico-chemical properties of soot/carbon aerosol particles, even the microphysical and environmental parameters involved in the transition of aerosol from gas phase to ice crystals in cloud should be considered. The contribution of phoretic forces should also be considered. After initial growth ice crystals can continue to grow by water vapour diffusion. Laboratory experiments confirm the contribution of diffusiophoresis with Stefan flow in the scavenging by snow crystals up to 3 mm in diameter. The particle scavenging efficiency of snow crystals is related to crystalline shape and depends on air relative humidity and temperature.

Early Warning of Snow Disaster in Pasturing Areas of Inner Mongolia

Using ground and remote sensing monitoring, and national standards of snow disaster monitoring evaluation standards, quantitative evaluation of snow disaster was realized. Threshold of animal husbandry weather forecast indexes were applied to establish snow disaster early warning for different grasslands in pasturing areas of Inner Mongolia, and make the grade distribution map of snow disaster early warning. The forecast results basically met the real conditions, it proved that this forecast method was capable of evaluating scale and influence degree of snow disaster. However, the snow disaster grade forecast deviated from the real conditions for the influence of weather forecast accuracy rate.

Potential Hazard Map for Snow Disaster Prevention Using GIS-Based Weighted Linear Combination Analysis and Remote Sensing Techniques: A Case Study in Northern Xinjiang, China

Snow disaster is one of the top ten natural disasters worldwide. Almost every year, there will be snow disasters in north Xinjiang, northwestern China. Since the accumulated heavy snow in winter season will seriously threaten people’s lives, the main object of this study is to produce a potential hazard map for snow avalanche prevention. Taking three snow seasons from November to March of year 2008 to 2010, potential hazard areas were estimated, based on snow volume products and terrain features. Remote sensing (RS) techniques and geographical information system (GIS) based weighted linear combination (WLC) approach were applied, taking into consideration multiple criteria. Snow avalanche risks were analyzed using physical exposure and vulnerability indexes. The analysis indicates that: the areas at high-risk of avalanches are located in the north and south part of the counties of Altay, Bortala and Ili prefectures;the areas at medium-risk of avalanches are found in the certain part of Altay prefecture and Urumqi, Changji, Tacheng prefectures;the avalanche risk is generally low throughout the large area to the certain part of the study area and the region on the border of the eastern north Xinjiang. Overall, the risks of snow avalanche in Altay and Ili prefectures are higher than that other regions;those areas should be allocated correspondingly more salvage materials.

Light-absorbing Particles in Snow and Ice: Measurement and Modeling of Climatic and Hydrological impact

Light absorbing particles（LAP, e.g., black carbon, brown carbon, and dust） influence water and energy budgets of the atmosphere and snowpack in multiple ways. In addition to their effects associated with atmospheric heating by absorption of solar radiation and interactions with clouds, LAP in snow on land and ice can reduce the surface reflectance（a.k.a., surface darkening）, which is likely to accelerate the snow aging process and further reduces snow albedo and increases the speed of snowpack melt. LAP in snow and ice（LAPSI） has been identified as one of major forcings affecting climate change, e.g.in the fourth and fifth assessment reports of IPCC. However, the uncertainty level in quantifying this effect remains very high. In this review paper, we document various technical methods of measuring LAPSI and review the progress made in measuring the LAPSI in Arctic, Tibetan Plateau and other mid-latitude regions. We also report the progress in modeling the mass concentrations, albedo reduction, radiative forcing, and climatic and hydrological impact of LAPSI at global and regional scales. Finally we identify some research needs for reducing the uncertainties in the impact of LAPSI on global and regional climate and the hydrological cycle.

The physical structures of snow and sea ice in the Arctic section of 150°-180°W during the summer of 2010

The physical structures of snow and sea ice in the Arctic section of 150°-180°W were observed on the basis of snow-pit, ice-core, and drill-hole measurements from late July to late August 2010. Almost all the in- vestigated floes were first-year ice, except for one located north of Alaska, which was probably multi-year ice transported from north of the Canadian Arctic Archipelago during early summer. The snow covers over all the investigated floes were in the melting phase, with temperatures approaching 0℃and densities of 295-398 kg/m3. The snow covers can be divided into two to five layers of different textures, with most cases having a top layer of fresh snow, a round-grain layer in the middle, and slush and/or thin icing layers at the bottom. The first-year sea ice contained about 7%-17% granular ice at the top. There was no granular ice in the lower layers. The interior melting and desalination of sea ice introduced strong stratifications of temper- ature, salinity, density, and gas and brine volume fractions. The sea ice temperature exhibited linear cooling with depth, while the salinity and the density increased linearly with normalized depth from 0.2 to 0.9 and from 0 to 0.65, respectively. The top layer, especially the freeboard layer, had the lowest salinity and density, and consequently the largest gas content and the smallest brine content. Both the salinity and density in the ice basal layer were highly scattered due to large differences in ice porosity among the samples. The bulk average sea ice temperature, salinity, density, and gas and brine volume fractions were -0.8℃, 1.8, 837 kg/m3, 9.3% and 10.4%, respectively. The snow cover, sea ice bottom, and sea ice interior show evidences of melting during mid-August in the investigated floe located at about 87°N, 175°W.

Observed and modelled snow and ice thickness in the Arctic Ocean with CHINARE buoy data

Sea ice and the snow pack on top of it were investigated using Chinese National Arctic Research Expedition （CHINARE） buoy data. Two polar hydrometeorological drifters, known as Zeno ice stations, were deployed during CHINARE 2003. A new type of high-resolution Snow and Ice Mass Balance Arrays, known as SIMBA buoys, were deployed during CHINARE 2014. Data from those buoys were applied to investigate the thickness of sea ice and snow in the CHINARE domain. A simple approach was applied to estimate the average snow thickness on the basis of Zeno temperature data. Snow and ice thicknesses were also derived from vertical temperature profile data based on the SIMBA buoys. A one-dimensional snow and ice thermodynamic model （HIGHTSI） was applied to calculate the snow and ice thickness along the buoy drift trajectories. The model forcing was based on forecasts and analyses of the European Centre for Medium-Range Weather Forecasts （ECMWF）. The Zeno buoys drifted in a confined area during 2003-2004. The snow thickness modelled applying HIGHTSI was consistent with results based on Zeno buoy data. The SIMBA buoys drifted from 81. 1°N, 157.4°W to 73.5°N, 134.9°W in 15 months during 2014-2015. The total ice thickness increased from an initial August 2014 value of 1.97 m to a maximum value of 2.45 in before the onset of snow melt in May 2015; the last observation was approximately 1 m in late November 2015. The ice thickness based on HIGHTSI agreed with SIMBA measurements, in particular when the seasonal variation of oceanic heat flux was taken into account, but the modelled snow thickness differed from the observed one. Sea ice thickness derived from SIMBA data was reasonably good in cold conditions, but challenges remain in both snow and ice thickness in summer.

Radiative Forcing and Climate Response Due to Black Carbon in Snow and Ice

The radiative forcing and climate response due to black carbon（BC） in snow and/or ice were investigated by integrating observed effects of BC on snow/ice albedo into an atmospheric general circulation model（BCC AGCM2.0.1） developed by the National Climate Center（NCC） of the China Meteorological Administration（CMA）.The results show that the global annual mean surface radiative forcing due to BC in snow/ice is ＋0.042 W m 2,with maximum forcing found over the Tibetan Plateau and regional mean forcing exceeding ＋2.8 W m 2.The global annual mean surface temperature increased 0.071 C due to BC in snow/ice.Positive surface radiative forcing was clearly shown in winter and spring and increased the surface temperature of snow/ice in the Northern Hemisphere.The surface temperatures of snow-covered areas of Eurasia and North America in winter（spring） increased by 0.83 C（0.6 C） and 0.83 C（0.46 C）,respectively.Snowmelt rates also increased greatly,leading to earlier snowmelt and peak runoff times.With the rise of surface temperatures in the Arctic,more water vapor could be released into the atmosphere,allowing easier cloud formation,which could lead to higher thermal emittance in the Arctic.However,the total cloud forcing could decrease due to increasing cloud cover,which will offset some of the positive feedback mechanism of the clouds.

Observation and modelling of snow and sea ice mass balance and its sensitivity to atmospheric forcing during spring and summer 2007 in the Central Arctic

Snow depth and sea ice thickness were observed applying an ice mass balance buoy(IMB)in the drifting ice station Tara during the International Polar Year in 2007.Detailed in situ observations on meteorological variables and surface fluxes were taken during May to August.For this study,the operational analyses and short-term forecasts from two numerical weather prediction(NWP)models(ECMWF and HIRLAM)were extracted for the Tara drift trajectory.We compared the IMB,meteorological and surface flux observations against the NWP products,also applying a one-dimensional thermodynamic sea ice model(HIGHTSI)to calculate the snow and ice mass balance and its sensitivity to atmospheric forcing.The modelled snow depth time series,controlled by NWP-based precipitation,was in line with the observed one.HIGHTSI reproduced well the snowmelt onset,the progress of the melt,and the first date of snow-free conditions.HIGHTSI performed well also in the late August freezing season.Challenges remain to model the“false bottom”observed during the melting season.The evolution of the vertical temperature profiles in snow and ice was better simulated when the model was forced by in situ observations instead of NWP results.During the melting period,the nonlinear ice temperature profile was successfully modelled with both forcing options.During spring and the melting season,total sea ice mass balance was most sensitive to uncertainties in NWP results for the downward longwave radiation,followed by the downward shortwave radiation,air temperature,and wind speed.

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.